@inproceedings{zhou-etal-2026-reat,
title = "{REAT}: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction",
author = "Zhou, Jianheng and
Zhang, Chaoli and
Wei, Xingjun and
Zhou, Xinliang and
Fortino, Giancarlo and
Fan, Xing and
Wang, Yanfeng and
Wen, Qingsong and
Li, Haoyang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.605/",
pages = "13945--13967",
ISBN = "979-8-89176-459-0",
abstract = "Current Large Language Models (LLMs) excel at solving complex mathematical problems, yet this proficiency does not inherently translate into effective tutoring. While advanced LLM tutors may leverage multi-agent frameworks or fine-tuning, most still lack a mechanism to systematically accumulate and reuse pedagogical experience over time, limiting their adaptability to diverse student needs during fluid, multi-turn interactions. To bridge this gap, we propose the Reflective Experience-Augmented Tutoring (REAT) framework, which couples experience distillation from historical dialogues with real-time adaptive retrieval. Driven by a multi-agent Observer-Critic-Mentor (OCM) distillation pipeline, REAT reviews past conversational trajectories and distills raw interactions into structured, problem-agnostic pedagogical experiences. During live tutoring, a state-aware retrieval module injects these curated experiences to provide adaptive scaffolding based on the student{'}s cognitive state. Experiments demonstrate that the proposed framework significantly outperforms both prompt-only and supervised fine-tuning (SFT) baselines, particularly in improving complex, low-scoring teaching scenarios. Crucially, the distilled experiences exhibit robust generalization across diverse model architectures and mathematical datasets."
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<abstract>Current Large Language Models (LLMs) excel at solving complex mathematical problems, yet this proficiency does not inherently translate into effective tutoring. While advanced LLM tutors may leverage multi-agent frameworks or fine-tuning, most still lack a mechanism to systematically accumulate and reuse pedagogical experience over time, limiting their adaptability to diverse student needs during fluid, multi-turn interactions. To bridge this gap, we propose the Reflective Experience-Augmented Tutoring (REAT) framework, which couples experience distillation from historical dialogues with real-time adaptive retrieval. Driven by a multi-agent Observer-Critic-Mentor (OCM) distillation pipeline, REAT reviews past conversational trajectories and distills raw interactions into structured, problem-agnostic pedagogical experiences. During live tutoring, a state-aware retrieval module injects these curated experiences to provide adaptive scaffolding based on the student’s cognitive state. Experiments demonstrate that the proposed framework significantly outperforms both prompt-only and supervised fine-tuning (SFT) baselines, particularly in improving complex, low-scoring teaching scenarios. Crucially, the distilled experiences exhibit robust generalization across diverse model architectures and mathematical datasets.</abstract>
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%0 Conference Proceedings
%T REAT: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction
%A Zhou, Jianheng
%A Zhang, Chaoli
%A Wei, Xingjun
%A Zhou, Xinliang
%A Fortino, Giancarlo
%A Fan, Xing
%A Wang, Yanfeng
%A Wen, Qingsong
%A Li, Haoyang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhou-etal-2026-reat
%X Current Large Language Models (LLMs) excel at solving complex mathematical problems, yet this proficiency does not inherently translate into effective tutoring. While advanced LLM tutors may leverage multi-agent frameworks or fine-tuning, most still lack a mechanism to systematically accumulate and reuse pedagogical experience over time, limiting their adaptability to diverse student needs during fluid, multi-turn interactions. To bridge this gap, we propose the Reflective Experience-Augmented Tutoring (REAT) framework, which couples experience distillation from historical dialogues with real-time adaptive retrieval. Driven by a multi-agent Observer-Critic-Mentor (OCM) distillation pipeline, REAT reviews past conversational trajectories and distills raw interactions into structured, problem-agnostic pedagogical experiences. During live tutoring, a state-aware retrieval module injects these curated experiences to provide adaptive scaffolding based on the student’s cognitive state. Experiments demonstrate that the proposed framework significantly outperforms both prompt-only and supervised fine-tuning (SFT) baselines, particularly in improving complex, low-scoring teaching scenarios. Crucially, the distilled experiences exhibit robust generalization across diverse model architectures and mathematical datasets.
%U https://aclanthology.org/2026.emnlp-main.605/
%P 13945-13967
Markdown (Informal)
[REAT: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction](https://aclanthology.org/2026.emnlp-main.605/) (Zhou et al., EMNLP 2026)
ACL
- Jianheng Zhou, Chaoli Zhang, Xingjun Wei, Xinliang Zhou, Giancarlo Fortino, Xing Fan, Yanfeng Wang, Qingsong Wen, and Haoyang Li. 2026. REAT: A Reflective Experience-Augmented Tutoring Framework for Multi-turn Mathematical Instruction. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 13945–13967, Budapest, Hungary. Association for Computational Linguistics.